Images may be obtained from a camera, the Internet or another source and stored as electronic data. In order for a user to identify shapes as they appear in still or moving images, the pixels associated with the electronically acquired data must be analyzed and the shapes identified. Constituent elements of an image, such as pixels, or groups of pixels, may be organized into meaningful groups. These elements are typically identified by known analytical processes. In particular, these elements may be organized and grouped to identify curved regions and boundaries. U.S. Pat. No. 7,734,112, which is incorporated by reference herein, describes this type of organization.
U.S. patent application Ser. No. 13/550,829, which is incorporated by reference herein, describes embodiments where images may be analyzed in different levels according to a “ladder of abstraction”. The primary level in the ladder involves analysis of pixels from an image. The next levels in the ladder involve analysis of grenze sets, curve primitives and/or region (area) primitives. Next in the ladder is a mezzanine level, a level between analysis of generic curve primitives (for example, cubic splines, curves), region primitives (for example, 3-dimensional surfaces, convex or concave surfaces), and the analysis of frank object models. A frank object model is a model of an object having utility value to human beings. Examples of frank object models in an image could include, but are not limited to, a face, the human form, cars, vehicles, aircraft, weapons, railroads, buildings, transportation infrastructure, natural artifacts (for example, plants, animals, fruit), cell, nuclei, organelles, and other living tissues.
At the mezzanine level, shapes are sought without regard to an object identity as it relates to human utility, and without regard to the pixel arrangement of the focal plane array. Shape classes at the mezzanine level may include, for example, ellipses, circles, straight lines, arcs (pieces of ellipses) and angle primitives. Shape characteristics, concave-side and convex-side color and shading characteristics, and possibly statistics (characteristic of texture) are used to guide a process of association. In the process of association, Bézier curves may be connected. Bézier curves are cubic splines that are connected into larger splines and that “continue” around whole compact objects without regard to pixel geometry.
In addition to Bézier curves, other geometric objects may also be derived from conventional pixel-based images, for example, by converting pixels into mathematical functions in order to obtain very accurate information about starting points, ending points, endpoint angles, and/or perpendicular directionality in two dimensions; by combining this very accurate spatial metadata with the information developed by currently-available 3-D remote sensing platforms in order to create very accurate measurements in space at a higher level of resolution than the current state of the art permits.
Accordingly, there is a need for a process and method for three dimensional data acquisition.
Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the present invention.
The apparatus and method components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.